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A model-based approach to the development and validation of control software for manufacturing equipment in the life sciences sector
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Date
2026-02
Abstract
The transition to Industry 4.0 presents both significant opportunities and challenges for manufacturers in the life sciences sector, particularly as personalised healthcare drives demand for flexible, software-driven manufacturing systems. Current equipment validation methods are rigid, documentation intensive processes which are ill-suited to the rapid reconfiguration and complexity required by modern production environments. This thesis demonstrates how Model-Based Design can provide a more flexible and automated approach to equipment control software validation that remains consistent with GMP and GAMP 5 guidelines.
Through a comprehensive evaluation of commercially available MBD platforms, MATLAB Simulink was identified as the most suitable tool for enabling automated software testing and code generation in regulated environments. A novel digital validation methodology was developed, using MBD to simulate, verify, and generate International Electrotechnical Commission (IEC) 61131-3-compliant PLC code, with full traceability from requirements to validation reports. This approach was successfully implemented on both new and existing equipment, including the development of a virtual test environment for automated regression testing.
In addition to equipment control validation, this research also explores the application of MBD in the development of process control algorithms. A case study is presented in which the algorithm for a Model Predictive Controller (MPC) is developed, validated and deployed on industry representative equipment to regulate pH levels in a continuous mixing process. This demonstrates the feasibility of using MBD not only to validate equipment functionality but also to design and verify control strategies for complex bioprocesses.
The research demonstrates that leveraging MBD tools during the development and validation process reduces validation costs, improves software quality, and supports agile development practices. The results demonstrate that applying MBD reduced validation effort by enabling automated test execution, established traceability between requirements and PLC code, and produced validated control algorithms that maintained stable pH regulation across varying operating scenarios. Collectively, these outcomes show that MBD can bridge regulatory compliance with the flexibility required for personalised healthcare manufacturing, enabling continuous equipment and process innovation without compromising product quality.
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Peer-reviewed
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University of Limerick
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McCarthy_2026_Model.pdf
Adobe PDF, 6.97 MB
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Sustainable Development Goals
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Attribution-NonCommercial-ShareAlike 4.0 International